title: Elastic net grouping variable selection combined with partial least squares regression (EN-PLSR) for the analysis of strongly multi-collinear spectroscopic data.
abstract: In this paper a novel wavelength region selection algorithm, called elastic net grouping variable selection combined with partial least squares regression (EN-PLSR), is proposed for multi-component spectral data analysis. The EN-PLSR algorithm can automatically select successive strongly correlated prediction variable groups related to the response variable using two steps. First, a portion of the correlated predictors are selected and divided into subgroups by means of the grouping effect of elastic net estimation. Then, a recursive leave-one-group-out strategy is employed to further shrink the variable groups in terms of the root mean square error of cross-validation (RMSECV) criterion. The performance of the algorithm with real near-infrared (NIR) spectroscopic data sets shows that the EN-PLSR algorithm is competitive with full-spectrum PLS and moving window partial least squares (MWPLS) regression methods and it is suitable for use with strongly correlated spectroscopic data.
authors.affiliation: School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083, PR China.
authors.forename: Guang-Hui
authors.initials: GH
authors.lastname: Fu
authors.forename: Qing-Song
authors.initials: QS
authors.lastname: Xu
authors.forename: Hong-Dong
authors.initials: HD
authors.lastname: Li
authors.forename: Dong-Sheng
authors.initials: DS
authors.lastname: Cao
authors.forename: Yi-Zeng
authors.initials: YZ
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005742:Gasoline; D016018:Least-Squares Analysis; D015203:Reproducibility of Results; D003313:Zea mays / Q000737:chemistry
chemical_list: D005742:Gasoline